Druck auf Anfrage Neuware - Printed after ordering - Nonlinear programming provides an excellent opportunity to explore an interesting variety of pure and solidly applicable mathematics, numerical analysis, and computing. This text develops some of the ideas and techniques involved in the optimization methods using calculus, leading to the study of convexity. This is followed by material on basic numerical methods, least squares, the Karush-Kuhn-Tucker theorem, penalty functions, and Lagrange multipliers. The authors have aimed their presentation at the student who has a working knowledge of matrix algebra and advanced calculus, but has had no previous exposure to optimization. N� de ref. del art�culo 9780387966144

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T�tulo: The Mathematics of Nonlinear Programming

Editorial: Springer New York, Springer US

A�o de publicaci�n: 1988

Encuadernaci�n: Buch

Condici�n: Neu

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Sinopsis:

NONLINEAR PROGRAMMING PROVIDES AN EXCELLENT OPPORTUNITY TO EXPLORE AN INTERESTING VARIETY OF PURE AND SOLIDLY APPLICABLE MATHEMATICS, NUMERICAL ANALYSIS, AND COMPUTING. THIS TEXT DEVELOPS SOME OF THE IDEAS AND TECHNIQUES INVOLVED IN THE OPTIMIZATION METHODS USING CALCULUS, LEADING TO THE STUDY OF CONVEXITY. THIS IS FOLLOWED BY MATERIAL ON BASIC NUMERICAL METHODS, LEAST SQUARES, THE KARUSH-KUHN-TUCKER THEOREM, PENALTY FUNCTIONS, AND LAGRANGE MULTIPLIERS. THE AUTHORS HAVE AIMED THEIR PRESENTATION AT THE STUDENT WHO HAS A WORKING KNOWLEDGE OF MATRIX ALGEBRA AND ADVANCED CALCULUS, BUT HAS HAD NO PREVIOUS EXPOSURE TO OPTIMIZATION.

Rese�a del editor:

Nonlinear programming provides an excellent opportunity to explore an interesting variety of pure and solidly applicable mathematics, numerical analysis, and computing. This text develops some of the ideas and techniques involved in the optimization methods using calculus, leading to the study of convexity. This is followed by material on basic numerical methods, least squares, the Karush-Kuhn-Tucker theorem, penalty functions, and Lagrange multipliers. The authors have aimed their presentation at the student who has a working knowledge of matrix algebra and advanced calculus, but has had no previous exposure to optimization.

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